Fakultät III - Medien, Information und Design
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Purpose: Radiology reports mostly contain free-text, which makes it challenging to obtain structured data. Natural language processing (NLP) techniques transform free-text reports into machine-readable document vectors that are important for creating reliable, scalable methods for data analysis. The aim of this study is to classify unstructured radiograph reports according to fractures of the distal fibula and to find the best text mining method.
Materials & Methods: We established a novel German language report dataset: a designated search engine was used to identify radiographs of the ankle and the reports were manually labeled according to fractures of the distal fibula. This data was used to establish a machine learning pipeline, which implemented the text representation methods bag-of-words (BOW), term frequency-inverse document frequency (TF-IDF), principal component analysis (PCA), non-negative matrix factorization (NMF), latent Dirichlet allocation (LDA), and document embedding (doc2vec). The extracted document vectors were used to train neural networks (NN), support vector machines (SVM), and logistic regression (LR) to recognize distal fibula fractures. The results were compared via cross-tabulations of the accuracy (acc) and area under the curve (AUC).
Results: In total, 3268 radiograph reports were included, of which 1076 described a fracture of the distal fibula. Comparison of the text representation methods showed that BOW achieved the best results (AUC = 0.98; acc = 0.97), followed by TF-IDF (AUC = 0.97; acc = 0.96), NMF (AUC = 0.93; acc = 0.92), PCA (AUC = 0.92; acc = 0.9), LDA (AUC = 0.91; acc = 0.89) and doc2vec (AUC = 0.9; acc = 0.88). When comparing the different classifiers, NN (AUC = 0,91) proved to be superior to SVM (AUC = 0,87) and LR (AUC = 0,85).
Conclusion: An automated classification of unstructured reports of radiographs of the ankle can reliably detect findings of fractures of the distal fibula. A particularly suitable feature extraction method is the BOW model.
Key Points:
- The aim was to classify unstructured radiograph reports according to distal fibula fractures.
- Our automated classification system can reliably detect fractures of the distal fibula.
- A particularly suitable feature extraction method is the BOW model.
The Wnt signaling pathway has been associated with many essential cell processes. This study aims to examine the effects of Wnt signaling on proliferation of cultured HEK293T cells. Cells were incubated with Wnt3a, and the activation of the Wnt pathway was followed by analysis of the level of the β-catenin protein and of the expression levels of the target genes MYC and CCND1. The level of β-catenin protein increased up to fourfold. While the mRNA levels of c-Myc and cyclin D1 increased slightly, the protein levels increased up to a factor of 1.5. Remarkably, MTT and BrdU assays showed different results when measuring the proliferation rate of Wnt3a stimulated HEK293T cells. In the BrdU assays an increase of the proliferation rate could be detected, which correlated to the applied Wnt3a concentration. Oppositely, this correlation could not be shown in the MTT assays. The MTT results, which are based on the mitochondrial activity, were confirmed by analysis of the succinate dehydrogenase complex by immunofluorescence and by western blotting. Taken together, our study shows that Wnt3a activates proliferation of HEK293 cells. These effects can be detected by measuring DNA synthesis rather than by measuring changes of mitochondrial activity.
Harmonisation of German Health Care Data Using the OMOP Common Data Model – A Practice Report
(2023)
Data harmonization is an important step in large-scale data analysis and for generating evidence on real world data in healthcare. With the OMOP common data model, a relevant instrument for data harmonization is available that is being promoted by different networks and communities. At the Hannover Medical School (MHH) in Germany, an Enterprise Clinical Research Data Warehouse (ECRDW) is established and harmonization of that data source is the focus of this work. We present MHH’s first implementation of the OMOP common data model on top of the ECRDW data source and demonstrate the challenges concerning the mapping of German healthcare terminologies to a standardized format.
This paper aims to provide a structured overview of four open, participatory formats that are particularly applicable in inquiry-based teaching and learning contexts: hackathons, book sprints, barcamps, and learning circles. Using examples, mostly from the work and experience context of the Open Science Lab at TIB Hannover, we address concrete processes, working methods, possible outcomes and challenges.
The compilation offers an introduction to the topic and is intended to provide tools for testing in practice.
Purpose: The calculation of aggregated composite measures is a widely used strategy to reduce the amount of data on hospital report cards. Therefore, this study aims to elicit and compare preferences of both patients as well as referring physicians regarding publicly available hospital quality information.
Methods: Based on systematic literature reviews as well as qualitative analysis, two discrete choice experiments (DCEs) were applied to elicit patients’ and referring physicians’ preferences. The DCEs were conducted using a fractional factorial design. Statistical data analysis was performed using multinomial logit models.
Results: Apart from five identical attributes, one specific attribute was identified for each study group, respectively. Overall, 322 patients (mean age 68.99) and 187 referring physicians (mean age 53.60) were included. Our models displayed significant coefficients for all attributes (p < 0.001 each). Among patients, “Postoperative complication rate” (20.6%; level range of 1.164) was rated highest, followed by “Mobility at hospital discharge” (19.9%; level range of 1.127), and ‘‘The number of cases treated” (18.5%; level range of 1.045). In contrast, referring physicians valued most the ‘‘One-year revision surgery rate’’ (30.4%; level range of 1.989), followed by “The number of cases treated” (21.0%; level range of 1.372), and “Postoperative complication rate” (17.2%; level range of 1.123).
Conclusion: We determined considerable differences between both study groups when calculating the relative value of publicly available hospital quality information. This may have an impact when calculating aggregated composite measures based on consumer-based weighting.
Chronic kidney disease is one of the main causes of mortality worldwide. It affects more than 800 million patients globally, accounting for approximately 10% of the general population. The significant burden of the disease prompts healthcare systems to implement adequate preventive and therapeutic measures. This systematic review and meta-analysis aimed to provide a concise summary of the findings published in the existing body of research about the influence that mobile health technology has on the outcomes of patients with the disease. A comprehensive systematic literature review was conducted from inception until March 1st, 2023. This systematic review and meta-analysis included all clinical trials that compared the efficacy of mobile app-based educational programs to that of more conventional educational treatment for the patients. Eleven papers were included in the current analysis, representing 759 CKD patients. 381 patients were randomly assigned to use the mobile apps, while 378 individuals were assigned to the control group. The mean systolic blood pressure was considerably lower in the mobile app group (MD -4.86; 95%-9.60, -0.13; p=0.04). Meanwhile, the mean level of satisfaction among patients who used the mobile app was considerably greater (MD 0.75; 95% CI 0.03, 1.46; p=0.04). Additionally, the mean self-management scores in the mobile app groups were significantly higher (SMD 0.534; 95% CI 0.201, 0.867; p=0.002). Mobile health applications are potentially valuable interventions for patients. This technology improved the self-management of the disease, reducing the mean levels of systolic blood pressure with a high degree of patient satisfaction.
Background: Autism Spectrum Disorder (ASD) is characterized by impairments in social communication, limited repetitive behaviors, impaired language development, and interest or activity patterns, which include a group complex neurodevelopmental syndrome with diverse phenotypes that reveal considerable etiological and clinical heterogeneity and are also considered one of the most heritable disorders (over 90%). Genetic, epigenetic, and environmental factors play a role in the development of ASD.
Aim: This study was designed to investigate the extent of DNA damage in parents of autistic children by treating peripheral blood mononuclear cells (PBMCs) with bleomycin and hydrogen peroxide (H2O2).
Methods: Peripheral blood mononuclear cells (PBMCs) were isolated by the Ficoll method and treated with a specific concentration of bleomycin and H2O2 for 30 min and 5 min, respectively. Then, the degree of DNA damage was analyzed by the alkaline comet assay or single cell gel electrophoresis (SCGE), an effective way to measure DNA fragmentation in eukaryotic cells.
Results: Our findings revealed that there is a significant difference in the increase of DNA damage in parents with affected children compared to the control group, which can indicate the inability of the DNA molecule repair system. Furthermore, our study showed a significant association between fathers’ occupational difficulties (exposed to the influence of environmental factors), as well as family marriage, and suffering from ASD in offspring.
Conclusion: Our results suggested that the influence of environmental factors on parents of autistic children may affect the development of autistic disorder in their offspring. Subsequently, based on our results, investigating the effect of environmental factors on the amount of DNA damage in parents with affected children requires more studies.
Appropriate data models are essential for the systematic collection, aggregation, and integration of health data and for subsequent analysis. However, recommendations for modeling health data are often not publicly available within specific projects. Therefore, the project Zukunftslabor Gesundheit investigates recommendations for modeling. Expert interviews with five experts were conducted and analyzed using qualitative content analysis. Based on the condensed categories “governance”, “modeling” and “standards”, the project team generated eight hypotheses for recommendations on health data modeling. In addition, relevant framework conditions such as different roles, international cooperation, education/training and political influence were identified. Although emerging from interviewing a small convenience sample of experts, the results help to plan more extensive data collections and to create recommendations for health data modeling.
Economic and political/governmental infrastructural factors are major contributors to the economic development/growth of all sectors of a country, such as in the area of healthcare systems and clinical research, including the pharmaceutical industry. But what is the interaction between economic, and political/governmental infrastructural factors and the development of healthcare systems, especially, the performance of the pharmaceutical industry? Information from selected articles of a literature search of PubMed and by using Google Advanced Search led to the generation of five categories of infrastructural factors, and were filled with data from 41 African Countries using the World Health Organization data repository. Median changes over time were given and tested by Wilcoxon signed-rank test and Friedman test, respectively. Analysis of factors related to availability of healthcare facilities showed that physicians and pharmacies were significant increased, with insignificantly decreased number of hospital beds. Healthcare Financing by the Government showed notable differences. Private health spending decreased significantly unlike Gross National Income. Analysis of infrastructural factors showed that stable supply of electricity and the associated use of the Internet improved significantly. The low level of data on the expansion of paved road networks suggests less developed medical services in remote rural areas. Healthcare systems in African countries improved over the last two decades, but differences between the individual countries still prevail and some of the countries cannot yet offer an attractive sales market for the products of pharmaceutical companies.
Monitoring of clinical trials is a fundamental process required by regulatory agencies. It assures the compliance of a center to the required regulations and the trial protocol. Traditionally, monitoring teams relied on extensive on-site visits and source data verification. However, this is costly, and the outcome is limited. Thus, central statistical monitoring (CSM) is an additional approach recently embraced by the International Council for Harmonisation (ICH) to detect problematic or erroneous data by using visualizations and statistical control measures. Existing implementations have been primarily focused on detecting inlier and outlier data. Other approaches include principal component analysis and distribution of the data. Here we focus on the utilization of comparisons of centers to the Grand mean for different model types and assumptions for common data types, such as binomial, ordinal, and continuous response variables. We implement the usage of multiple comparisons of single centers to the Grand mean of all centers. This approach is also available for various non-normal data types that are abundant in clinical trials. Further, using confidence intervals, an assessment of equivalence to the Grand mean can be applied. In a Monte Carlo simulation study, the applied statistical approaches have been investigated for their ability to control type I error and the assessment of their respective power for balanced and unbalanced designs which are common in registry data and clinical trials. Data from the German Multiple Sclerosis Registry (GMSR) including proportions of missing data, adverse events and disease severity scores were used to verify the results on Real-World-Data (RWD).